Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations18287
Missing cells0
Missing cells (%)0.0%
Duplicate rows220
Duplicate rows (%)1.2%
Total size in memory2.7 MiB
Average record size in memory152.0 B

Variable types

Numeric5
Text5
Categorical8
Boolean1

Alerts

Dataset has 220 (1.2%) duplicate rowsDuplicates
Drive wheels is highly overall correlated with ManufacturerHigh correlation
Manufacturer is highly overall correlated with Drive wheelsHigh correlation
Doors is highly imbalanced (82.5%) Imbalance
Wheel is highly imbalanced (61.9%) Imbalance
Price is highly skewed (γ1 = 133.6244339) Skewed
Airbags has 2318 (12.7%) zeros Zeros

Reproduction

Analysis started2025-02-06 09:08:26.553184
Analysis finished2025-02-06 09:08:30.335294
Duration3.78 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct17974
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45589491
Minimum20746880
Maximum45816654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:30.413177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20746880
5-th percentile44844298
Q145714960
median45772371
Q345802236
95-th percentile45813712
Maximum45816654
Range25069774
Interquartile range (IQR)87276

Descriptive statistics

Standard deviation914948.7
Coefficient of variation (CV)0.02006929
Kurtosis223.25811
Mean45589491
Median Absolute Deviation (MAD)33219
Skewness-12.56015
Sum8.3369503 × 1011
Variance8.3713112 × 1011
MonotonicityNot monotonic
2025-02-06T15:08:30.511252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45815365 8
 
< 0.1%
45815361 8
 
< 0.1%
45815363 7
 
< 0.1%
45815368 7
 
< 0.1%
45723475 7
 
< 0.1%
45815359 6
 
< 0.1%
45815366 6
 
< 0.1%
45815372 6
 
< 0.1%
45786759 6
 
< 0.1%
45720276 6
 
< 0.1%
Other values (17964) 18220
99.6%
ValueCountFrequency (%)
20746880 1
< 0.1%
23242980 1
< 0.1%
24367759 1
< 0.1%
24701923 1
< 0.1%
24940334 1
< 0.1%
25368573 1
< 0.1%
26248496 1
< 0.1%
26327387 1
< 0.1%
26465408 1
< 0.1%
26556126 1
< 0.1%
ValueCountFrequency (%)
45816654 1
< 0.1%
45816651 1
< 0.1%
45816650 1
< 0.1%
45816648 1
< 0.1%
45816647 1
< 0.1%
45816646 1
< 0.1%
45816645 1
< 0.1%
45816635 1
< 0.1%
45816629 1
< 0.1%
45816627 1
< 0.1%

Price
Real number (ℝ)

Skewed 

Distinct2274
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18229.537
Minimum1
Maximum26307500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:30.597177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile314
Q15018
median12858
Q321953
95-th percentile48871.2
Maximum26307500
Range26307499
Interquartile range (IQR)16935

Descriptive statistics

Standard deviation195191.78
Coefficient of variation (CV)10.707445
Kurtosis17997.803
Mean18229.537
Median Absolute Deviation (MAD)8311
Skewness133.62443
Sum3.3336355 × 108
Variance3.8099829 × 1010
MonotonicityNot monotonic
2025-02-06T15:08:30.696727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470 272
 
1.5%
15681 259
 
1.4%
392 241
 
1.3%
314 234
 
1.3%
14113 228
 
1.2%
12544 221
 
1.2%
10976 209
 
1.1%
7840 206
 
1.1%
17249 204
 
1.1%
18817 201
 
1.1%
Other values (2264) 16012
87.6%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 15
 
0.1%
6 6
 
< 0.1%
9 1
 
< 0.1%
19 1
 
< 0.1%
20 7
 
< 0.1%
25 16
 
0.1%
28 1
 
< 0.1%
30 77
0.4%
31 13
 
0.1%
ValueCountFrequency (%)
26307500 1
 
< 0.1%
308906 1
 
< 0.1%
260296 1
 
< 0.1%
250574 1
 
< 0.1%
228935 1
 
< 0.1%
194438 1
 
< 0.1%
172486 4
< 0.1%
167781 1
 
< 0.1%
156805 2
< 0.1%
153669 1
 
< 0.1%

Levy
Text

Distinct531
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:30.928705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.6576803
Min length1

Characters and Unicode

Total characters48601
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)0.9%

Sample

1st row1399
2nd row1018
3rd row-
4th row862
5th row446
ValueCountFrequency (%)
5195
28.4%
765 473
 
2.6%
891 454
 
2.5%
639 403
 
2.2%
640 401
 
2.2%
781 294
 
1.6%
1017 289
 
1.6%
707 269
 
1.5%
642 260
 
1.4%
836 258
 
1.4%
Other values (521) 9991
54.6%
2025-02-06T15:08:31.228534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7622
15.7%
7 5622
11.6%
- 5195
10.7%
6 4220
8.7%
5 4204
8.7%
8 4127
8.5%
9 4031
8.3%
3 3988
8.2%
0 3977
8.2%
4 3007
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7622
15.7%
7 5622
11.6%
- 5195
10.7%
6 4220
8.7%
5 4204
8.7%
8 4127
8.5%
9 4031
8.3%
3 3988
8.2%
0 3977
8.2%
4 3007
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7622
15.7%
7 5622
11.6%
- 5195
10.7%
6 4220
8.7%
5 4204
8.7%
8 4127
8.5%
9 4031
8.3%
3 3988
8.2%
0 3977
8.2%
4 3007
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7622
15.7%
7 5622
11.6%
- 5195
10.7%
6 4220
8.7%
5 4204
8.7%
8 4127
8.5%
9 4031
8.3%
3 3988
8.2%
0 3977
8.2%
4 3007
 
6.2%

Manufacturer
Categorical

High correlation 

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
HYUNDAI
3730 
TOYOTA
3566 
MERCEDES-BENZ
1906 
FORD
1060 
CHEVROLET
1045 
Other values (45)
6980 

Length

Max length13
Median length10
Mean length6.8169191
Min length3

Characters and Unicode

Total characters124661
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLEXUS
2nd rowCHEVROLET
3rd rowHONDA
4th rowFORD
5th rowHONDA

Common Values

ValueCountFrequency (%)
HYUNDAI 3730
20.4%
TOYOTA 3566
19.5%
MERCEDES-BENZ 1906
10.4%
FORD 1060
 
5.8%
CHEVROLET 1045
 
5.7%
LEXUS 962
 
5.3%
BMW 938
 
5.1%
HONDA 937
 
5.1%
NISSAN 613
 
3.4%
VOLKSWAGEN 537
 
2.9%
Other values (40) 2993
16.4%

Length

2025-02-06T15:08:31.298246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hyundai 3730
20.4%
toyota 3566
19.5%
mercedes-benz 1906
10.4%
ford 1060
 
5.8%
chevrolet 1045
 
5.7%
lexus 962
 
5.2%
bmw 938
 
5.1%
honda 937
 
5.1%
nissan 613
 
3.3%
volkswagen 537
 
2.9%
Other values (41) 3034
16.6%

Most occurring characters

ValueCountFrequency (%)
E 12193
 
9.8%
O 11949
 
9.6%
A 11490
 
9.2%
N 9404
 
7.5%
T 8587
 
6.9%
D 8362
 
6.7%
Y 7761
 
6.2%
S 6477
 
5.2%
I 6139
 
4.9%
H 6067
 
4.9%
Other values (17) 36232
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 124661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 12193
 
9.8%
O 11949
 
9.6%
A 11490
 
9.2%
N 9404
 
7.5%
T 8587
 
6.9%
D 8362
 
6.7%
Y 7761
 
6.2%
S 6477
 
5.2%
I 6139
 
4.9%
H 6067
 
4.9%
Other values (17) 36232
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 124661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 12193
 
9.8%
O 11949
 
9.6%
A 11490
 
9.2%
N 9404
 
7.5%
T 8587
 
6.9%
D 8362
 
6.7%
Y 7761
 
6.2%
S 6477
 
5.2%
I 6139
 
4.9%
H 6067
 
4.9%
Other values (17) 36232
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 124661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 12193
 
9.8%
O 11949
 
9.6%
A 11490
 
9.2%
N 9404
 
7.5%
T 8587
 
6.9%
D 8362
 
6.7%
Y 7761
 
6.2%
S 6477
 
5.2%
I 6139
 
4.9%
H 6067
 
4.9%
Other values (17) 36232
29.1%

Model
Text

Distinct648
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:31.545311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length18
Mean length5.6716247
Min length1

Characters and Unicode

Total characters103717
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRX 450
2nd rowEquinox
3rd rowFIT
4th rowEscape
5th rowFIT
ValueCountFrequency (%)
prius 1229
 
5.4%
sonata 1096
 
4.8%
camry 1005
 
4.4%
350 957
 
4.2%
elantra 956
 
4.2%
e 820
 
3.6%
fe 535
 
2.4%
santa 535
 
2.4%
fit 450
 
2.0%
h1 437
 
1.9%
Other values (506) 14669
64.7%
2025-02-06T15:08:31.861633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11151
 
10.8%
r 6760
 
6.5%
t 6036
 
5.8%
n 5606
 
5.4%
i 4420
 
4.3%
4402
 
4.2%
o 4285
 
4.1%
s 3948
 
3.8%
0 3673
 
3.5%
e 3667
 
3.5%
Other values (67) 49769
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103717
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11151
 
10.8%
r 6760
 
6.5%
t 6036
 
5.8%
n 5606
 
5.4%
i 4420
 
4.3%
4402
 
4.2%
o 4285
 
4.1%
s 3948
 
3.8%
0 3673
 
3.5%
e 3667
 
3.5%
Other values (67) 49769
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103717
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11151
 
10.8%
r 6760
 
6.5%
t 6036
 
5.8%
n 5606
 
5.4%
i 4420
 
4.3%
4402
 
4.2%
o 4285
 
4.1%
s 3948
 
3.8%
0 3673
 
3.5%
e 3667
 
3.5%
Other values (67) 49769
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103717
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11151
 
10.8%
r 6760
 
6.5%
t 6036
 
5.8%
n 5606
 
5.4%
i 4420
 
4.3%
4402
 
4.2%
o 4285
 
4.1%
s 3948
 
3.8%
0 3673
 
3.5%
e 3667
 
3.5%
Other values (67) 49769
48.0%

Prod. year
Real number (ℝ)

Distinct49
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.077
Minimum1939
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:31.961340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile2000
Q12009
median2012
Q32015
95-th percentile2017
Maximum2020
Range81
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.4573327
Coefficient of variation (CV)0.0027136368
Kurtosis9.719557
Mean2011.077
Median Absolute Deviation (MAD)3
Skewness-1.9285114
Sum36776566
Variance29.78248
MonotonicityNot monotonic
2025-02-06T15:08:32.046307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2012 2085
11.4%
2014 2052
11.2%
2013 1884
10.3%
2011 1556
 
8.5%
2015 1494
 
8.2%
2016 1442
 
7.9%
2010 1432
 
7.8%
2017 932
 
5.1%
2008 689
 
3.8%
2009 577
 
3.2%
Other values (39) 4144
22.7%
ValueCountFrequency (%)
1939 3
< 0.1%
1953 2
< 0.1%
1957 1
 
< 0.1%
1964 1
 
< 0.1%
1965 2
< 0.1%
1968 1
 
< 0.1%
1973 1
 
< 0.1%
1974 2
< 0.1%
1977 1
 
< 0.1%
1978 1
 
< 0.1%
ValueCountFrequency (%)
2020 44
 
0.2%
2019 293
 
1.6%
2018 475
 
2.6%
2017 932
5.1%
2016 1442
7.9%
2015 1494
8.2%
2014 2052
11.2%
2013 1884
10.3%
2012 2085
11.4%
2011 1556
8.5%

Category
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
Sedan
8356 
Jeep
5258 
Hatchback
2702 
Minivan
 
605
Coupe
 
461
Other values (6)
905 

Length

Max length11
Median length9
Mean length5.5682725
Min length4

Characters and Unicode

Total characters101827
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJeep
2nd rowJeep
3rd rowHatchback
4th rowJeep
5th rowHatchback

Common Values

ValueCountFrequency (%)
Sedan 8356
45.7%
Jeep 5258
28.8%
Hatchback 2702
 
14.8%
Minivan 605
 
3.3%
Coupe 461
 
2.5%
Universal 337
 
1.8%
Microbus 278
 
1.5%
Goods wagon 211
 
1.2%
Pickup 44
 
0.2%
Cabriolet 26
 
0.1%

Length

2025-02-06T15:08:32.146928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sedan 8356
45.2%
jeep 5258
28.4%
hatchback 2702
 
14.6%
minivan 605
 
3.3%
coupe 461
 
2.5%
universal 337
 
1.8%
microbus 278
 
1.5%
goods 211
 
1.1%
wagon 211
 
1.1%
pickup 44
 
0.2%
Other values (2) 35
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 19705
19.4%
a 14939
14.7%
n 10123
9.9%
d 8567
8.4%
S 8356
8.2%
p 5763
 
5.7%
c 5726
 
5.6%
J 5258
 
5.2%
b 3006
 
3.0%
k 2746
 
2.7%
Other values (20) 17638
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19705
19.4%
a 14939
14.7%
n 10123
9.9%
d 8567
8.4%
S 8356
8.2%
p 5763
 
5.7%
c 5726
 
5.6%
J 5258
 
5.2%
b 3006
 
3.0%
k 2746
 
2.7%
Other values (20) 17638
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19705
19.4%
a 14939
14.7%
n 10123
9.9%
d 8567
8.4%
S 8356
8.2%
p 5763
 
5.7%
c 5726
 
5.6%
J 5258
 
5.2%
b 3006
 
3.0%
k 2746
 
2.7%
Other values (20) 17638
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19705
19.4%
a 14939
14.7%
n 10123
9.9%
d 8567
8.4%
S 8356
8.2%
p 5763
 
5.7%
c 5726
 
5.6%
J 5258
 
5.2%
b 3006
 
3.0%
k 2746
 
2.7%
Other values (20) 17638
17.3%

Leather interior
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
True
13412 
False
4875 
ValueCountFrequency (%)
True 13412
73.3%
False 4875
 
26.7%
2025-02-06T15:08:32.193864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Fuel type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
Petrol
9508 
Diesel
3874 
Hybrid
3499 
LPG
 
870
CNG
 
458
Other values (2)
 
78

Length

Max length14
Median length6
Mean length5.8159348
Min length3

Characters and Unicode

Total characters106356
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHybrid
2nd rowPetrol
3rd rowPetrol
4th rowHybrid
5th rowPetrol

Common Values

ValueCountFrequency (%)
Petrol 9508
52.0%
Diesel 3874
21.2%
Hybrid 3499
 
19.1%
LPG 870
 
4.8%
CNG 458
 
2.5%
Plug-in Hybrid 77
 
0.4%
Hydrogen 1
 
< 0.1%

Length

2025-02-06T15:08:32.263547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T15:08:32.326703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
petrol 9508
51.8%
diesel 3874
21.1%
hybrid 3576
 
19.5%
lpg 870
 
4.7%
cng 458
 
2.5%
plug-in 77
 
0.4%
hydrogen 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 17257
16.2%
l 13459
12.7%
r 13085
12.3%
P 10455
9.8%
o 9509
8.9%
t 9508
8.9%
i 7527
7.1%
D 3874
 
3.6%
s 3874
 
3.6%
H 3577
 
3.4%
Other values (12) 14231
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17257
16.2%
l 13459
12.7%
r 13085
12.3%
P 10455
9.8%
o 9509
8.9%
t 9508
8.9%
i 7527
7.1%
D 3874
 
3.6%
s 3874
 
3.6%
H 3577
 
3.4%
Other values (12) 14231
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17257
16.2%
l 13459
12.7%
r 13085
12.3%
P 10455
9.8%
o 9509
8.9%
t 9508
8.9%
i 7527
7.1%
D 3874
 
3.6%
s 3874
 
3.6%
H 3577
 
3.4%
Other values (12) 14231
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17257
16.2%
l 13459
12.7%
r 13085
12.3%
P 10455
9.8%
o 9509
8.9%
t 9508
8.9%
i 7527
7.1%
D 3874
 
3.6%
s 3874
 
3.6%
H 3577
 
3.4%
Other values (12) 14231
13.4%

Engine volume
Text

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:32.557959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0060152
Min length1

Characters and Unicode

Total characters54971
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st row3.5
2nd row3
3rd row1.3
4th row2.5
5th row1.3
ValueCountFrequency (%)
2 3806
19.1%
2.5 2300
11.5%
1.8 1862
9.3%
turbo 1640
8.2%
1.6 1517
 
7.6%
1.5 1330
 
6.7%
3.5 1236
 
6.2%
2.4 988
 
5.0%
3 782
 
3.9%
1.3 511
 
2.6%
Other values (56) 3955
19.8%
2025-02-06T15:08:32.742908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 13421
24.4%
2 8957
16.3%
1 6204
11.3%
5 5129
 
9.3%
3 3675
 
6.7%
4 2352
 
4.3%
6 2062
 
3.8%
8 1995
 
3.6%
1640
 
3.0%
b 1640
 
3.0%
Other values (7) 7896
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 13421
24.4%
2 8957
16.3%
1 6204
11.3%
5 5129
 
9.3%
3 3675
 
6.7%
4 2352
 
4.3%
6 2062
 
3.8%
8 1995
 
3.6%
1640
 
3.0%
b 1640
 
3.0%
Other values (7) 7896
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 13421
24.4%
2 8957
16.3%
1 6204
11.3%
5 5129
 
9.3%
3 3675
 
6.7%
4 2352
 
4.3%
6 2062
 
3.8%
8 1995
 
3.6%
1640
 
3.0%
b 1640
 
3.0%
Other values (7) 7896
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 13421
24.4%
2 8957
16.3%
1 6204
11.3%
5 5129
 
9.3%
3 3675
 
6.7%
4 2352
 
4.3%
6 2062
 
3.8%
8 1995
 
3.6%
1640
 
3.0%
b 1640
 
3.0%
Other values (7) 7896
14.4%

Mileage
Text

Distinct7467
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:32.912711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length9
Mean length8.4638814
Min length4

Characters and Unicode

Total characters154779
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5584 ?
Unique (%)30.5%

Sample

1st row186005 km
2nd row192000 km
3rd row200000 km
4th row168966 km
5th row91901 km
ValueCountFrequency (%)
km 18287
50.0%
0 691
 
1.9%
200000 167
 
0.5%
150000 150
 
0.4%
180000 107
 
0.3%
100000 107
 
0.3%
160000 102
 
0.3%
1000 96
 
0.3%
120000 91
 
0.2%
170000 88
 
0.2%
Other values (7458) 16688
45.6%
2025-02-06T15:08:33.159559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29373
19.0%
18287
11.8%
k 18287
11.8%
m 18287
11.8%
1 14756
9.5%
2 9672
 
6.2%
5 7066
 
4.6%
3 7027
 
4.5%
4 6718
 
4.3%
8 6568
 
4.2%
Other values (3) 18738
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29373
19.0%
18287
11.8%
k 18287
11.8%
m 18287
11.8%
1 14756
9.5%
2 9672
 
6.2%
5 7066
 
4.6%
3 7027
 
4.5%
4 6718
 
4.3%
8 6568
 
4.2%
Other values (3) 18738
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29373
19.0%
18287
11.8%
k 18287
11.8%
m 18287
11.8%
1 14756
9.5%
2 9672
 
6.2%
5 7066
 
4.6%
3 7027
 
4.5%
4 6718
 
4.3%
8 6568
 
4.2%
Other values (3) 18738
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29373
19.0%
18287
11.8%
k 18287
11.8%
m 18287
11.8%
1 14756
9.5%
2 9672
 
6.2%
5 7066
 
4.6%
3 7027
 
4.5%
4 6718
 
4.3%
8 6568
 
4.2%
Other values (3) 18738
12.1%

Cylinders
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5662492
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:33.222396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1780069
Coefficient of variation (CV)0.25798131
Kurtosis6.6608559
Mean4.5662492
Median Absolute Deviation (MAD)0
Skewness2.1222437
Sum83503
Variance1.3877004
MonotonicityNot monotonic
2025-02-06T15:08:33.288812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 13789
75.4%
6 3224
 
17.6%
8 911
 
5.0%
5 151
 
0.8%
3 93
 
0.5%
2 39
 
0.2%
1 31
 
0.2%
12 29
 
0.2%
10 10
 
0.1%
16 5
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
1 31
 
0.2%
2 39
 
0.2%
3 93
 
0.5%
4 13789
75.4%
5 151
 
0.8%
6 3224
 
17.6%
7 3
 
< 0.1%
8 911
 
5.0%
9 1
 
< 0.1%
10 10
 
0.1%
ValueCountFrequency (%)
16 5
 
< 0.1%
14 1
 
< 0.1%
12 29
 
0.2%
10 10
 
0.1%
9 1
 
< 0.1%
8 911
 
5.0%
7 3
 
< 0.1%
6 3224
 
17.6%
5 151
 
0.8%
4 13789
75.4%

Gear box type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
Automatic
13176 
Tiptronic
2761 
Manual
1640 
Variator
 
710

Length

Max length9
Median length9
Mean length8.692131
Min length6

Characters and Unicode

Total characters158953
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutomatic
2nd rowTiptronic
3rd rowVariator
4th rowAutomatic
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Automatic 13176
72.1%
Tiptronic 2761
 
15.1%
Manual 1640
 
9.0%
Variator 710
 
3.9%

Length

2025-02-06T15:08:33.359184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T15:08:33.423142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
automatic 13176
72.1%
tiptronic 2761
 
15.1%
manual 1640
 
9.0%
variator 710
 
3.9%

Most occurring characters

ValueCountFrequency (%)
t 29823
18.8%
i 19408
12.2%
a 17876
11.2%
o 16647
10.5%
c 15937
10.0%
u 14816
9.3%
A 13176
8.3%
m 13176
8.3%
n 4401
 
2.8%
r 4181
 
2.6%
Other values (5) 9512
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 158953
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 29823
18.8%
i 19408
12.2%
a 17876
11.2%
o 16647
10.5%
c 15937
10.0%
u 14816
9.3%
A 13176
8.3%
m 13176
8.3%
n 4401
 
2.8%
r 4181
 
2.6%
Other values (5) 9512
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 158953
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 29823
18.8%
i 19408
12.2%
a 17876
11.2%
o 16647
10.5%
c 15937
10.0%
u 14816
9.3%
A 13176
8.3%
m 13176
8.3%
n 4401
 
2.8%
r 4181
 
2.6%
Other values (5) 9512
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 158953
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 29823
18.8%
i 19408
12.2%
a 17876
11.2%
o 16647
10.5%
c 15937
10.0%
u 14816
9.3%
A 13176
8.3%
m 13176
8.3%
n 4401
 
2.8%
r 4181
 
2.6%
Other values (5) 9512
 
6.0%

Drive wheels
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
Front
12444 
4x4
3795 
Rear
2048 

Length

Max length5
Median length5
Mean length4.4729589
Min length3

Characters and Unicode

Total characters81797
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4x4
2nd row4x4
3rd rowFront
4th row4x4
5th rowFront

Common Values

ValueCountFrequency (%)
Front 12444
68.0%
4x4 3795
 
20.8%
Rear 2048
 
11.2%

Length

2025-02-06T15:08:33.507768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T15:08:33.560361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
front 12444
68.0%
4x4 3795
 
20.8%
rear 2048
 
11.2%

Most occurring characters

ValueCountFrequency (%)
r 14492
17.7%
F 12444
15.2%
o 12444
15.2%
n 12444
15.2%
t 12444
15.2%
4 7590
9.3%
x 3795
 
4.6%
R 2048
 
2.5%
e 2048
 
2.5%
a 2048
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 14492
17.7%
F 12444
15.2%
o 12444
15.2%
n 12444
15.2%
t 12444
15.2%
4 7590
9.3%
x 3795
 
4.6%
R 2048
 
2.5%
e 2048
 
2.5%
a 2048
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 14492
17.7%
F 12444
15.2%
o 12444
15.2%
n 12444
15.2%
t 12444
15.2%
4 7590
9.3%
x 3795
 
4.6%
R 2048
 
2.5%
e 2048
 
2.5%
a 2048
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 14492
17.7%
F 12444
15.2%
o 12444
15.2%
n 12444
15.2%
t 12444
15.2%
4 7590
9.3%
x 3795
 
4.6%
R 2048
 
2.5%
e 2048
 
2.5%
a 2048
 
2.5%

Doors
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
04-May
17519 
02-Mar
 
654
>5
 
114

Length

Max length6
Median length6
Mean length5.9750643
Min length2

Characters and Unicode

Total characters109266
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04-May
2nd row04-May
3rd row04-May
4th row04-May
5th row04-May

Common Values

ValueCountFrequency (%)
04-May 17519
95.8%
02-Mar 654
 
3.6%
>5 114
 
0.6%

Length

2025-02-06T15:08:33.641534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T15:08:33.692200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
04-may 17519
95.8%
02-mar 654
 
3.6%
5 114
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 18173
16.6%
- 18173
16.6%
a 18173
16.6%
M 18173
16.6%
4 17519
16.0%
y 17519
16.0%
2 654
 
0.6%
r 654
 
0.6%
> 114
 
0.1%
5 114
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18173
16.6%
- 18173
16.6%
a 18173
16.6%
M 18173
16.6%
4 17519
16.0%
y 17519
16.0%
2 654
 
0.6%
r 654
 
0.6%
> 114
 
0.1%
5 114
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18173
16.6%
- 18173
16.6%
a 18173
16.6%
M 18173
16.6%
4 17519
16.0%
y 17519
16.0%
2 654
 
0.6%
r 654
 
0.6%
> 114
 
0.1%
5 114
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18173
16.6%
- 18173
16.6%
a 18173
16.6%
M 18173
16.6%
4 17519
16.0%
y 17519
16.0%
2 654
 
0.6%
r 654
 
0.6%
> 114
 
0.1%
5 114
 
0.1%

Wheel
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
Left wheel
16932 
Right-hand drive
 
1355

Length

Max length16
Median length10
Mean length10.444578
Min length10

Characters and Unicode

Total characters191000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeft wheel
2nd rowLeft wheel
3rd rowRight-hand drive
4th rowLeft wheel
5th rowLeft wheel

Common Values

ValueCountFrequency (%)
Left wheel 16932
92.6%
Right-hand drive 1355
 
7.4%

Length

2025-02-06T15:08:33.760371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-06T15:08:33.807251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
left 16932
46.3%
wheel 16932
46.3%
right-hand 1355
 
3.7%
drive 1355
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 52151
27.3%
h 19642
 
10.3%
18287
 
9.6%
t 18287
 
9.6%
L 16932
 
8.9%
f 16932
 
8.9%
w 16932
 
8.9%
l 16932
 
8.9%
i 2710
 
1.4%
d 2710
 
1.4%
Other values (7) 9485
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 52151
27.3%
h 19642
 
10.3%
18287
 
9.6%
t 18287
 
9.6%
L 16932
 
8.9%
f 16932
 
8.9%
w 16932
 
8.9%
l 16932
 
8.9%
i 2710
 
1.4%
d 2710
 
1.4%
Other values (7) 9485
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 52151
27.3%
h 19642
 
10.3%
18287
 
9.6%
t 18287
 
9.6%
L 16932
 
8.9%
f 16932
 
8.9%
w 16932
 
8.9%
l 16932
 
8.9%
i 2710
 
1.4%
d 2710
 
1.4%
Other values (7) 9485
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 52151
27.3%
h 19642
 
10.3%
18287
 
9.6%
t 18287
 
9.6%
L 16932
 
8.9%
f 16932
 
8.9%
w 16932
 
8.9%
l 16932
 
8.9%
i 2710
 
1.4%
d 2710
 
1.4%
Other values (7) 9485
 
5.0%

Color
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
Black
4768 
White
4299 
Silver
3626 
Grey
2271 
Blue
1312 
Other values (11)
2011 

Length

Max length13
Median length5
Mean length5.0531525
Min length3

Characters and Unicode

Total characters92407
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilver
2nd rowBlack
3rd rowBlack
4th rowWhite
5th rowSilver

Common Values

ValueCountFrequency (%)
Black 4768
26.1%
White 4299
23.5%
Silver 3626
19.8%
Grey 2271
12.4%
Blue 1312
 
7.2%
Red 608
 
3.3%
Green 294
 
1.6%
Orange 246
 
1.3%
Brown 177
 
1.0%
Carnelian red 165
 
0.9%
Other values (6) 521
 
2.8%

Length

2025-02-06T15:08:33.877080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 4768
25.7%
white 4299
23.2%
silver 3626
19.5%
grey 2271
12.2%
blue 1423
 
7.7%
red 773
 
4.2%
green 294
 
1.6%
orange 246
 
1.3%
brown 177
 
1.0%
carnelian 165
 
0.9%
Other values (6) 521
 
2.8%

Most occurring characters

ValueCountFrequency (%)
e 13896
15.0%
l 10351
11.2%
i 8231
 
8.9%
r 6980
 
7.6%
B 6375
 
6.9%
a 5344
 
5.8%
k 4902
 
5.3%
c 4768
 
5.2%
W 4299
 
4.7%
t 4299
 
4.7%
Other values (19) 22962
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13896
15.0%
l 10351
11.2%
i 8231
 
8.9%
r 6980
 
7.6%
B 6375
 
6.9%
a 5344
 
5.8%
k 4902
 
5.3%
c 4768
 
5.2%
W 4299
 
4.7%
t 4299
 
4.7%
Other values (19) 22962
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13896
15.0%
l 10351
11.2%
i 8231
 
8.9%
r 6980
 
7.6%
B 6375
 
6.9%
a 5344
 
5.8%
k 4902
 
5.3%
c 4768
 
5.2%
W 4299
 
4.7%
t 4299
 
4.7%
Other values (19) 22962
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13896
15.0%
l 10351
11.2%
i 8231
 
8.9%
r 6980
 
7.6%
B 6375
 
6.9%
a 5344
 
5.8%
k 4902
 
5.3%
c 4768
 
5.2%
W 4299
 
4.7%
t 4299
 
4.7%
Other values (19) 22962
24.8%

Airbags
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5599606
Minimum0
Maximum16
Zeros2318
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:33.939611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q312
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.332649
Coefficient of variation (CV)0.66046875
Kurtosis-1.3393651
Mean6.5599606
Median Absolute Deviation (MAD)3
Skewness0.095636022
Sum119962
Variance18.771847
MonotonicityNot monotonic
2025-02-06T15:08:34.007898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 5708
31.2%
12 5409
29.6%
0 2318
12.7%
8 1455
 
8.0%
6 1176
 
6.4%
2 974
 
5.3%
10 774
 
4.2%
5 95
 
0.5%
16 90
 
0.5%
7 74
 
0.4%
Other values (7) 214
 
1.2%
ValueCountFrequency (%)
0 2318
12.7%
1 65
 
0.4%
2 974
 
5.3%
3 34
 
0.2%
4 5708
31.2%
5 95
 
0.5%
6 1176
 
6.4%
7 74
 
0.4%
8 1455
 
8.0%
9 56
 
0.3%
ValueCountFrequency (%)
16 90
 
0.5%
15 6
 
< 0.1%
14 19
 
0.1%
13 2
 
< 0.1%
12 5409
29.6%
11 32
 
0.2%
10 774
 
4.2%
9 56
 
0.3%
8 1455
 
8.0%
7 74
 
0.4%

Manufacturer_Model
Text

Distinct652
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size143.0 KiB
2025-02-06T15:08:34.223616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length27
Mean length13.488544
Min length6

Characters and Unicode

Total characters246665
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLEXUS RX 450
2nd rowCHEVROLET Equinox
3rd rowHONDA FIT
4th rowFORD Escape
5th rowHONDA FIT
ValueCountFrequency (%)
hyundai 3730
 
9.1%
toyota 3566
 
8.7%
mercedes-benz 1906
 
4.6%
prius 1229
 
3.0%
sonata 1096
 
2.7%
ford 1060
 
2.6%
chevrolet 1045
 
2.5%
camry 1005
 
2.5%
lexus 962
 
2.3%
350 957
 
2.3%
Other values (553) 24461
59.6%
2025-02-06T15:08:34.540620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22730
 
9.2%
E 15195
 
6.2%
A 13014
 
5.3%
O 12695
 
5.1%
a 11151
 
4.5%
T 10513
 
4.3%
N 9719
 
3.9%
S 8857
 
3.6%
D 8467
 
3.4%
Y 7802
 
3.2%
Other values (67) 126522
51.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 246665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22730
 
9.2%
E 15195
 
6.2%
A 13014
 
5.3%
O 12695
 
5.1%
a 11151
 
4.5%
T 10513
 
4.3%
N 9719
 
3.9%
S 8857
 
3.6%
D 8467
 
3.4%
Y 7802
 
3.2%
Other values (67) 126522
51.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 246665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22730
 
9.2%
E 15195
 
6.2%
A 13014
 
5.3%
O 12695
 
5.1%
a 11151
 
4.5%
T 10513
 
4.3%
N 9719
 
3.9%
S 8857
 
3.6%
D 8467
 
3.4%
Y 7802
 
3.2%
Other values (67) 126522
51.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 246665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22730
 
9.2%
E 15195
 
6.2%
A 13014
 
5.3%
O 12695
 
5.1%
a 11151
 
4.5%
T 10513
 
4.3%
N 9719
 
3.9%
S 8857
 
3.6%
D 8467
 
3.4%
Y 7802
 
3.2%
Other values (67) 126522
51.3%

Interactions

2025-02-06T15:08:29.548318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:27.827953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.217161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.616408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.149045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.632745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:27.903738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.300620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.699828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.215657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.717649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:27.984169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.383644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.782916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.298780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.796670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.067362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.466935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.882741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.381826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.895617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.150660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.533314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:28.968721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-06T15:08:29.465350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-06T15:08:34.603126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AirbagsCategoryColorCylindersDoorsDrive wheelsFuel typeGear box typeIDLeather interiorManufacturerPriceProd. yearWheel
Airbags1.0000.1520.1030.2100.1250.2910.1980.337-0.0340.4760.266-0.0760.1680.320
Category0.1521.0000.0970.1250.4280.4360.2360.3400.1130.3810.2990.0640.1720.331
Color0.1030.0971.0000.0690.0840.1260.1870.1300.0210.2080.1120.0000.1070.130
Cylinders0.2100.1250.0691.0000.0610.4690.0880.137-0.0730.2080.277-0.050-0.1590.090
Doors0.1250.4280.0840.0611.0000.1600.0750.2050.0520.1270.2060.0370.1630.009
Drive wheels0.2910.4360.1260.4690.1601.0000.1820.2390.0390.0930.6130.0000.2340.026
Fuel type0.1980.2360.1870.0880.0750.1821.0000.2300.0100.1920.3320.0000.1650.143
Gear box type0.3370.3400.1300.1370.2050.2390.2301.0000.0260.4740.4020.0200.3250.224
ID-0.0340.1130.021-0.0730.0520.0390.0100.0261.0000.1000.1140.0340.0330.098
Leather interior0.4760.3810.2080.2080.1270.0930.1920.4740.1001.0000.4700.0000.3740.354
Manufacturer0.2660.2990.1120.2770.2060.6130.3320.4020.1140.4701.0000.0000.4630.448
Price-0.0760.0640.000-0.0500.0370.0000.0000.0200.0340.0000.0001.0000.2870.000
Prod. year0.1680.1720.107-0.1590.1630.2340.1650.3250.0330.3740.4630.2871.0000.233
Wheel0.3200.3310.1300.0900.0090.0260.1430.2240.0980.3540.4480.0000.2331.000

Missing values

2025-02-06T15:08:30.033664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-06T15:08:30.191232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDPriceLevyManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsManufacturer_Model
045654403133281399LEXUSRX 4502010JeepYesHybrid3.5186005 km6.0Automatic4x404-MayLeft wheelSilver12LEXUS RX 450
144731507166211018CHEVROLETEquinox2011JeepNoPetrol3192000 km6.0Tiptronic4x404-MayLeft wheelBlack8CHEVROLET Equinox
2457744198467-HONDAFIT2006HatchbackNoPetrol1.3200000 km4.0VariatorFront04-MayRight-hand driveBlack2HONDA FIT
3457691853607862FORDEscape2011JeepYesHybrid2.5168966 km4.0Automatic4x404-MayLeft wheelWhite0FORD Escape
44580926311726446HONDAFIT2014HatchbackYesPetrol1.391901 km4.0AutomaticFront04-MayLeft wheelSilver4HONDA FIT
54580291239493891HYUNDAISanta FE2016JeepYesDiesel2160931 km4.0AutomaticFront04-MayLeft wheelWhite4HYUNDAI Santa FE
6456567681803761TOYOTAPrius2010HatchbackYesHybrid1.8258909 km4.0AutomaticFront04-MayLeft wheelWhite12TOYOTA Prius
745816158549751HYUNDAISonata2013SedanYesPetrol2.4216118 km4.0AutomaticFront04-MayLeft wheelGrey12HYUNDAI Sonata
8456413951098394TOYOTACamry2014SedanYesHybrid2.5398069 km4.0AutomaticFront04-MayLeft wheelBlack12TOYOTA Camry
94575683926657-LEXUSRX 3502007JeepYesPetrol3.5128500 km6.0Automatic4x404-MayLeft wheelSilver12LEXUS RX 350
IDPriceLevyManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsManufacturer_Model
1827745769427297931053MERCEDES-BENZE 3502014SedanYesDiesel3.5219030 km6.0Automatic4x404-MayLeft wheelBlack12MERCEDES-BENZ E 350
18278457737267061850MERCEDES-BENZE 3502008SedanYesDiesel3.5122874 km6.0AutomaticRear04-MayLeft wheelBlack12MERCEDES-BENZ E 350
182793997739550-TOYOTAPrius2008HatchbackNoHybrid1.5150000 km4.0AutomaticFront04-MayLeft wheelSilver6TOYOTA Prius
1828045760891470645TOYOTAPrius2011HatchbackYesHybrid1.8307325 km4.0AutomaticFront04-MayLeft wheelSilver12TOYOTA Prius
182814577230658021055MERCEDES-BENZE 3502013SedanYesDiesel3.5107800 km6.0AutomaticRear04-MayLeft wheelGrey12MERCEDES-BENZ E 350
18282457983558467-MERCEDES-BENZCLK 2001999CoupeYesCNG2.0 Turbo300000 km4.0ManualRear02-MarLeft wheelSilver5MERCEDES-BENZ CLK 200
182834577885615681831HYUNDAISonata2011SedanYesPetrol2.4161600 km4.0TiptronicFront04-MayLeft wheelRed8HYUNDAI Sonata
182844580499726108836HYUNDAITucson2010JeepYesDiesel2116365 km4.0AutomaticFront04-MayLeft wheelGrey4HYUNDAI Tucson
182854579352653311288CHEVROLETCaptiva2007JeepYesDiesel251258 km4.0AutomaticFront04-MayLeft wheelBlack4CHEVROLET Captiva
1828645813273470753HYUNDAISonata2012SedanYesHybrid2.4186923 km4.0AutomaticFront04-MayLeft wheelWhite12HYUNDAI Sonata

Duplicate rows

Most frequently occurring

IDPriceLevyManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsManufacturer_Model# duplicates
1904581536114113-TOYOTAAqua2013HatchbackNoHybrid1.5100000 km4.0VariatorFront04-MayRight-hand driveWhite6TOYOTA Aqua8
192458153657213-MAZDADemio evropuli2003HatchbackNoCNG1.4185000 km4.0ManualFront04-MayLeft wheelBlue4MAZDA Demio evropuli8
364572347519444502FORDFusion2013SedanNoPetrol1.5 Turbo100000 km4.0AutomaticFront04-MayLeft wheelBlack8FORD Fusion7
191458153638781-TOYOTAIst2002HatchbackNoPetrol1.5117000 km4.0Automatic4x404-MayRight-hand driveRed4TOYOTA Ist7
194458153686899-HONDAStream2004MinivanNoPetrol1.70 km4.0AutomaticFront04-MayRight-hand driveSilver4HONDA Stream7
3545720276156811055MERCEDES-BENZE 3502013SedanYesPetrol3.5 Turbo140800 km6.0Automatic4x404-MayLeft wheelBlack0MERCEDES-BENZ E 3506
874578675970562-BMWX5 3.52014JeepYesPetrol3.0 Turbo196000 km6.0Tiptronic4x404-MayLeft wheelBlack12BMW X5 3.56
18845815359471493LEXUSRX 3502016JeepYesPetrol3.565981 km6.0AutomaticFront04-MayLeft wheelBlack12LEXUS RX 3506
1934581536623521-FORDTransit1996MicrobusNoDiesel2.5123000 km4.0ManualRear04-MayLeft wheelWhite2FORD Transit6
195458153727840-MERCEDES-BENZE 2001998SedanNoCNG2180003 km4.0ManualRear04-MayLeft wheelBlack4MERCEDES-BENZ E 2006